Modern communication systems often use packet based routers (e.g., IP networks, ad hoc sensor networks, etc.) and the routers can become overwhelmed when the volume of data to be transferred exceeds the available communication bandwidth. As a result, traffic control and traffic congestion avoidance systems are becoming increasingly popular and necessary to control this network traffic.
A safe design option is to avoid traffic congestion all together by over-designing the communication links to carry worst case traffic loads on all links. However, a weakness of the option is that this is wasteful of bandwidth when each node is not using its full allocation. Additionally the implementation costs associated with such a design methodology may be prohibitive.
A more typical approach is more statistical in nature, where the expected level and typical variations in traffic loads and data source characteristics are used to size inter-node links or share a common link resource (e.g. bandwidth). For example typical statistical multiplexing divides a communications link into an arbitrary number of variable bit-rate digital channels or data streams with each data source receiving an a-priori allocated a capacity based on its assumed statistics. A weakness in this approach is that often limited accurate data or models are available; hence the system may perform unacceptably poorly under actual traffic conditions. Typical performance impacts of congested inter-node communication include latent data transfers due to buffering messages for a less congested time, expanded buffer memory in nodes to hold messages for transmission in less congested times, or just dropping packets completely which causes the source to request a retransmission (e.g., IP packet transfer).
Similar to standard statistical multiplexing are systems that dynamically allocate link resources (e.g. bandwidth) based on prevailing conditions. These algorithms are commonly referred to as dynamic bandwidth allocation (DBA). Dynamic bandwidth allocation takes advantage of several attributes of shared networks. Typically, all users are not connected to the network at one time, but even if all users are connected, they are not transmitting data (or voice or video) at all times. Also, most traffic is “bursty,” i.e., there are gaps between packets of information that can be filled with other user traffic. Other general techniques for overcoming problems associated with network congestion include Resource Auction Multiple Access (RAMA); Demand Assignable Multiple Access (DAMA); Random Access (aloha/slotted aloha); Bandwidth-on-Demand (BOD); Quality of Service (QOS) guarantee; and a number of different reservation protocols.
Still other prior art techniques control the networking system say by employing data labels or prioritization schemes. For example, some users may charged more to have certain service guarantees (e.g. maximum latency) through the network so when their data arrives a label in the packet indicates the high priority nature of the data for transmission. Other methods use a demand assignment device to allocate resources to each user, with the bit rates authorized by a congestion controller subsystem that operates globally for all connections supported by the user station. These techniques manage network flow, but do not control data generated from the data sources or the thresholds of any data sensors associated with the data sources.
All prior art systems in this subject matter deal with control of network traffic and managing congestion of data already within the network. Part of the novelty of this invention is the co-ordination of congestion control by dynamically linking the data generation (e.g. detection thresholds) in a distributed sensor system sharing a common communication link.
Hence, the current invention will provide a useful mechanism to allocate the transfer of data from a plurality of nodes to a central node over a shared access medium or link while maintaining the overall best possible (global) sensitivity at the detection nodes using a novel data adaptive thresholding technique.